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Cited by 3,573

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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Svetnik, Vladimir, Liaw, Andy, Tong, Christopher, Culberson, J. Christopher, Sheridan, Robert P., Feuston, Bradley P.·2003·Journal of Chemical Information and Computer Sciences
DOI 10.1021/ci034160g
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Abstract

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

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Metadata

Authors
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P.
Venue
Journal of Chemical Information and Computer Sciences
Publisher
American Chemical Society (ACS)
Published
2003-11-01
Volume
43
Issue
6
Pages
1947-1958
ISSN
0095-2338 (ISSN-L)
DOI
10.1021/ci034160g
OpenAlex
W1988195734
Cited by
3,573
References
3
FWCI
18.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. https://app.cochat.ai/papers/10.1021/ci034160g
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Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003). Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. Journal of Chemical Information and Computer Sciences. https://doi.org/10.1021/ci034160g

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